如何在TensorFlow中添加正则化? [英] How to add regularizations in TensorFlow?

问题描述

我发现在许多使用TensorFlow实现的神经网络代码中，正则化项通常是通过在损失值上手动添加一个附加项来实现的.

我的问题是:

1. 是否有比手动完成更为优雅或推荐的正规化方法?

2. 我还发现get_variable具有参数regularizer.应该如何使用?根据我的观察，如果我们向其传递正则化器(例如tf.contrib.layers.l2_regularizer，将计算表示正则化项的张量并将其添加到名为tf.GraphKeys.REGULARIZATOIN_LOSSES的图集合中.该集合是否会被TensorFlow自动使用(例如，培训时使用优化程序)还是希望我自己使用该集合?

如第二点所述，建议使用regularizer参数.您可以在get_variable中使用它，也可以在variable_scope中将其设置一次，并对所有变量进行规范化.

损失在图中收集，您需要像这样将它们手动添加到成本函数中.

  reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
  reg_constant = 0.01  # Choose an appropriate one.
  loss = my_normal_loss + reg_constant * sum(reg_losses)

希望有帮助！

I found in many available neural network code implemented using TensorFlow that regularization terms are often implemented by manually adding an additional term to loss value.

My questions are:

1. Is there a more elegant or recommended way of regularization than doing it manually?

2. I also find that get_variable has an argument regularizer. How should it be used? According to my observation, if we pass a regularizer to it (such as tf.contrib.layers.l2_regularizer, a tensor representing regularized term will be computed and added to a graph collection named tf.GraphKeys.REGULARIZATOIN_LOSSES. Will that collection be automatically used by TensorFlow (e.g. used by optimizers when training)? Or is it expected that I should use that collection by myself?

解决方案

As you say in the second point, using the regularizer argument is the recommended way. You can use it in get_variable, or set it once in your variable_scope and have all your variables regularized.

The losses are collected in the graph, and you need to manually add them to your cost function like this.

  reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
  reg_constant = 0.01  # Choose an appropriate one.
  loss = my_normal_loss + reg_constant * sum(reg_losses)

Hope that helps!

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